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# --------------------------------------------------------
# InstructDiffusion
# Based on instruct-pix2pix (https://github.com/timothybrooks/instruct-pix2pix)
# Modified by Binxin Yang (tennyson@mail.ustc.edu.cn)
# --------------------------------------------------------
from __future__ import annotations
import os
import random
import copy
import json
import math
from pathlib import Path
from typing import Any
import numpy as np
import torch
import torchvision
from einops import rearrange
from PIL import Image
from torch.utils.data import Dataset
from dataset.seg.grefcoco import G_REFER
class GrefCOCODataset(Dataset):
def __init__(
self,
path: str,
split: str = "train",
min_resize_res: int = 256,
max_resize_res: int = 256,
crop_res: int = 256,
flip_prob: float = 0.0,
transparency: float = 0.0,
test: bool = False,
):
assert split in ("train", "val", "test")
self.path = path
self.min_resize_res = min_resize_res
self.max_resize_res = max_resize_res
self.crop_res = crop_res
self.flip_prob = flip_prob
self.G_ref_dataset=G_REFER(data_root=path)
self.IMAGE_DIR = os.path.join(path, 'images/train2014')
self.list_ref=self.G_ref_dataset.getRefIds(split=split)
self.transparency = transparency
self.test = test
seg_diverse_prompt_path = 'dataset/prompt/prompt_seg.txt'
self.seg_diverse_prompt_list=[]
with open(seg_diverse_prompt_path) as f:
line=f.readline()
while line:
line=line.strip('\n')
self.seg_diverse_prompt_list.append(line)
line=f.readline()
color_list_file_path='dataset/prompt/color_list_train_small.txt'
self.color_list=[]
with open(color_list_file_path) as f:
line = f.readline()
while line:
line_split = line.strip('\n').split(" ")
if len(line_split)>1:
temp = []
for i in range(4):
temp.append(line_split[i])
self.color_list.append(temp)
line = f.readline()
def __len__(self) -> int:
return len(self.list_ref)
def _augmentation_new(self, image, label):
# Cropping
h, w = label.shape
if h > w:
start_h = random.randint(0, h - w)
end_h = start_h + w
image = image[start_h:end_h]
label = label[start_h:end_h]
elif h < w:
start_w = random.randint(0, w - h)
end_w = start_w + h
image = image[:, start_w:end_w]
label = label[:, start_w:end_w]
else:
pass
image = Image.fromarray(image).resize((self.min_resize_res, self.min_resize_res), resample=Image.Resampling.LANCZOS)
image = np.asarray(image, dtype=np.uint8)
label = Image.fromarray(label).resize((self.min_resize_res, self.min_resize_res), resample=Image.Resampling.NEAREST)
label = np.asarray(label, dtype=np.int64)
return image, label
def __getitem__(self, i: int) -> dict[str, Any]:
ref_ids = self.list_ref[i]
ref = self.G_ref_dataset.loadRefs(ref_ids)[0]
sentences = random.choice(ref['sentences'])['sent']
prompt = random.choice(self.seg_diverse_prompt_list)
color = random.choice(self.color_list)
color_name = color[0]
prompt = prompt.format(color=color_name.lower(), object=sentences.lower())
R, G, B = color[3].split(",")
R = int(R)
G = int(G)
B = int(B)
image_name = self.G_ref_dataset.loadImgs(ref['image_id'])[0]['file_name']
image_path = os.path.join(self.IMAGE_DIR,image_name)
mask = self.G_ref_dataset.getMaskByRef(ref=ref,merge=True)['mask']
image = Image.open(image_path).convert("RGB")
image = np.asarray(image)
image, mask = self._augmentation_new(image,mask)
mask = (mask == 1)
image_0 = Image.fromarray(image)
image_1 = copy.deepcopy(image)
image_1[:,:,0][mask]=self.transparency*image_1[:,:,0][mask]+(1-self.transparency)*R
image_1[:,:,1][mask]=self.transparency*image_1[:,:,1][mask]+(1-self.transparency)*G
image_1[:,:,2][mask]=self.transparency*image_1[:,:,2][mask]+(1-self.transparency)*B
image_1 = Image.fromarray(image_1)
reize_res = torch.randint(self.min_resize_res, self.max_resize_res + 1, ()).item()
image_0 = image_0.resize((reize_res, reize_res), Image.Resampling.LANCZOS)
image_1 = image_1.resize((reize_res, reize_res), Image.Resampling.LANCZOS)
image_0 = rearrange(2 * torch.tensor(np.array(image_0)).float() / 255 - 1, "h w c -> c h w")
image_1 = rearrange(2 * torch.tensor(np.array(image_1)).float() / 255 - 1, "h w c -> c h w")
crop = torchvision.transforms.RandomCrop(self.crop_res)
flip = torchvision.transforms.RandomHorizontalFlip(float(self.flip_prob))
image_0, image_1 = flip(crop(torch.cat((image_0, image_1)))).chunk(2)
mask = torch.tensor(mask).float()
crop = torchvision.transforms.RandomCrop(self.crop_res)
flip = torchvision.transforms.RandomHorizontalFlip(float(self.flip_prob))
image_0, image_1 = flip(crop(torch.cat((image_0, image_1)))).chunk(2)
return dict(edited=image_1, edit=dict(c_concat=image_0, c_crossattn=prompt)) |